A digital image includes pixels with various levels of intensity. For example a gray-scale image includes pixels that are various shades of gray ranging from black to white. A digital image can be converted to a bitonal or binary image, in which pixels are either black or white, using a process known as thresholding. Thresholding can be useful in separating foreground features of an image, such as handwriting and printed text, from background features, such as background patterns or noise. As result, the text can be detected and/or made more readable for humans or optical character recognition (OCR) techniques. However, some thresholding techniques may not be effective at distinguishing foreground features from background features. In particular, foreground intensity levels in some check images may not differ significantly from background intensity levels due to noise or widely varying intensities in the background and/or foreground. Thus, some background features can remain as residual background noise after thresholding has been performed.
Reducing or removing residual background noise in a binary image without materially affecting the desirable elements of the image can be difficult. One type of binary image is a binary image of a check. A binary image of a check may include one or more text regions, such as a payee area, amount payable area(s), and/or signature area. Excessive noise in a binary image will negatively affect detection of text or other objects and/or legibility of the binary image.